We conduct extensive experiments to demonstrate the merits of our proposed AFNO transformer. Namely, 1) we evaluate the efficiencyy-accuracy trade-off between AFNO and alternative mixing mechanisms on inpainting and classification pretraining tasks; and then 2) measure performance on few-shot semantic segmentation with inpainting pretraining; and 3) evaluate the performance of AFNO in high resolution settings with semantic segmentation. Our experiments cover a wide-range of datasets, including ImageNet-1k, CelebA-Faces, LSUN-Cats, ADE-Cars, and Cityscapes as in [1]}, [2]}, [3]}, [4]}, [5]}.
